超越二元:人口预测中的性别和性别多样性

Q3 Decision Sciences
Peta Darby, Rachel Jeffreson
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引用次数: 0

摘要

对许多人来说,他们的性别与出生时记录的性别相同。对一些人来说,出生时记录的性别和性别可能不一致,或者他们可能不完全属于男性或女性的二元类别。人们日益认识到,需要在二元性和性别以外的范围内对人口进行高质量的估计和预测。然而,目前关于这一主题的人口统计学文献很少,此类数据的编制也很有限。在本文中,我们使用人口方程作为框架来描述在人口预测生产中考虑性别和性别多样性的影响。在此过程中,我们考虑了对基本人口估计数、出生、死亡和移徙的影响。我们还考虑了承认性别是一个可以随着时间而改变的概念的含义。我们概述了现有的澳大利亚和国际数据收集方法,并解决了对形成预测假设的影响。最后,我们概述了未来形成人口预测的可能方向,这些预测将性别和社会性别考虑在二元之外。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beyond the binary: Sex and gender diversity in population projections
For many people, their gender is the same as their sex recorded at birth. For some, gender and sex recorded at birth may not align, or they may not fall exclusively into the binary categories of male or female. There is growing recognition of the need to have quality estimates and projections of the population in a context beyond binary sex and gender. However, there is currently little demographic literature on this topic and production of such data is limited. In this paper, we use the demographic equation as a framework to describe the implications of considering sex and gender diversity in the production of population projections. In doing so, we consider implications for base population estimates, births, deaths and migration. We also consider implications of acknowledging gender as a concept that can change over time. We outline existing Australian and international approaches to data collection and address implications for the formation of projection assumptions. We conclude by outlining possible future directions for forming population projections that consider sex and gender beyond the binary.
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来源期刊
Statistical Journal of the IAOS
Statistical Journal of the IAOS Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
1.30
自引率
0.00%
发文量
116
期刊介绍: This is the flagship journal of the International Association for Official Statistics and is expected to be widely circulated and subscribed to by individuals and institutions in all parts of the world. The main aim of the Journal is to support the IAOS mission by publishing articles to promote the understanding and advancement of official statistics and to foster the development of effective and efficient official statistical services on a global basis. Papers are expected to be of wide interest to readers. Such papers may or may not contain strictly original material. All papers are refereed.
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